The present invention relates to nodule segmentation, and more particularly, to ground glass nodule (GGN) segmentation in pulmonary computed tomographic (CT) volumes using a Markov random field.
Ground glass nodules (GGNs) are, for example, radiographic appearances of hazy lung opacities not associated with an obscuration of underlying vessels. GGNs come in two forms, “pure” and “mixed” as shown in FIG. 1. Pure GGNs do not comprise any solid components, whereas mixed GGNs comprise some solid components.
GGNs are more clearly shown in high resolution computed tomographic (HRCT) images than plain radiographs. GGNs also appear differently than solid nodules in HRCT images because solid nodules have a higher contrast and well defined boundaries. In addition, the appearance of GGNs in HRCT images is a highly significant finding as they often indicate the presence of an active and potentially treatable process such as bronchiolalveolar carcinomas or invasive adenocarcinoma.
Because GGNs are typically associated with active lung disease, the presence of GGNs often leads to further diagnostic evaluation, including, for example, lung biopsy. Thus, a computer-based segmentation can be of assistance to medical experts for diagnosis and treatment of certain types of lung disease. Accordingly, there is a need for a system and method of computer-based segmentation that can be used to accurately and consistently segment GGNs for quick diagnosis.